-------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /accounts/grad/haowen.wu/Documents/EJR-revise/dynamic/check_May201
> 9/mlogit-full-after-Aug2017.log
  log type:  text
 opened on:  25 May 2019, 20:36:15

. model_transition,job(0)
From Purely Professional

Iteration 0:   log pseudolikelihood = -26327.291  
Iteration 1:   log pseudolikelihood = -22347.651  
Iteration 2:   log pseudolikelihood =  -21944.28  
Iteration 3:   log pseudolikelihood = -21937.831  
Iteration 4:   log pseudolikelihood = -21937.817  
Iteration 5:   log pseudolikelihood = -21937.817  

Multinomial logistic regression                 Number of obs     =     27,913
                                                Wald chi2(94)     =   10081.63
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -21937.817               Pseudo R2         =     0.1667

                          (Std. Err. adjusted for 5,232 clusters in title_id1)
------------------------------------------------------------------------------
             |               Robust
  transition |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
  female_lag |
          0  |  -.7727552   .1944328    -3.97   0.000    -1.153837   -.3916738
          1  |  -.1022739   .3783516    -0.27   0.787    -.8438294    .6392817
             |
title_female |
          0  |  -.0966431   .0515369    -1.88   0.061    -.1976536    .0043674
          1  |  -.1682223   .1105088    -1.52   0.128    -.3848156    .0483711
             |
  female_lag#|
title_female |
        0 0  |   .0663286   .1038339     0.64   0.523    -.1371822    .2698394
        0 1  |   .0231549   .2170751     0.11   0.915    -.4023045    .4486143
        1 0  |   .5554919   .2922681     1.90   0.057    -.0173431    1.128327
        1 1  |     .32401   .2156056     1.50   0.133    -.0985692    .7465893
             |
0.title_p_~y |  -.0539142   .0396228    -1.36   0.174    -.1315734     .023745
             |
  female_lag#|
title_p_ac~y |
        0 0  |   .0520099   .0903928     0.58   0.565    -.1251568    .2291766
        1 0  |   .1095691    .168388     0.65   0.515    -.2204653    .4396034
             |
1.title_pe~y |  -.0154954   .0746456    -0.21   0.836    -.1617981    .1308073
             |
  female_lag#|
title_pers~y |
        0 1  |  -.3385023   .1935212    -1.75   0.080    -.7177969    .0407922
        1 1  |  -.5848895   .3126479    -1.87   0.061    -1.197668    .0278891
             |
first_female |
          0  |  -.1153036   .0371746    -3.10   0.002    -.1881644   -.0424428
          1  |  -.2147887   .0780141    -2.75   0.006    -.3676935   -.0618839
             |
  female_lag#|
first_female |
        0 0  |   .1867021   .0896336     2.08   0.037     .0110234    .3623808
        0 1  |   .4310014   .1709252     2.52   0.012     .0959941    .7660087
        1 0  |  -.0521144   .2245761    -0.23   0.816    -.4922753    .3880466
        1 1  |    .049723   .1988795     0.25   0.803    -.3400736    .4395196
             |
0.first_p_~y |   .3381318   .0394026     8.58   0.000     .2609042    .4153594
             |
  female_lag#|
first_p_ac~y |
        0 0  |   .2160077   .1019731     2.12   0.034      .016144    .4158714
        1 0  |   .1170324   .1958052     0.60   0.550    -.2667387    .5008034
             |
1.first_pe~y |  -.1701926   .0545242    -3.12   0.002     -.277058   -.0633273
             |
  female_lag#|
first_pers~y |
        0 1  |   -.123677   .1355827    -0.91   0.362    -.3894141    .1420602
        1 1  |   .1302426   .2646561     0.49   0.623    -.3884737     .648959
             |
group_p_ac~y |   5.848855   .1007899    58.03   0.000     5.651311      6.0464
             |
  female_lag#|
          c. |
group_p_ac~y |
          0  |   1.017699   .2559821     3.98   0.000     .5159833    1.519415
          1  |   .1053308    .484798     0.22   0.828    -.8448559    1.055518
             |
group_pers~y |   3.196002   .1996659    16.01   0.000     2.804664     3.58734
             |
  female_lag#|
          c. |
group_pers~y |
          0  |    .485225   .4687837     1.04   0.301    -.4335742    1.404024
          1  |  -.5846395   .8217612    -0.71   0.477    -2.195262    1.025983
             |
job_rank_lag |
          1  |  -.0457961    .070356    -0.65   0.515    -.1836914    .0920992
          2  |    .094415    .085573     1.10   0.270     -.073305    .2621351
          3  |  -.0424681    .086744    -0.49   0.624    -.2124832    .1275471
          4  |   .0138625   .1666256     0.08   0.934    -.3127176    .3404426
             |
  female_lag#|
job_rank_lag |
        0 1  |   -.020473   .1494376    -0.14   0.891    -.3133654    .2724194
        0 2  |  -.0332719   .1931068    -0.17   0.863    -.4117543    .3452105
        0 3  |  -.0316772   .1808595    -0.18   0.861    -.3861552    .3228008
        0 4  |  -.0136722   .2077229    -0.07   0.948    -.4208017    .3934573
        1 1  |    .005051   .3016265     0.02   0.987    -.5861262    .5962281
        1 2  |   .2908622   .3196634     0.91   0.363    -.3356666     .917391
        1 3  |   .2035054     .29901     0.68   0.496    -.3825434    .7895542
        1 4  |   .2742294    .348011     0.79   0.431    -.4078596    .9563184
             |
    ln_post2 |    .173793   .0173762    10.00   0.000     .1397363    .2078497
             |
  female_lag#|
  c.ln_post2 |
          0  |   .0136643   .0383986     0.36   0.722    -.0615957    .0889242
          1  |   -.082491   .0693963    -1.19   0.235    -.2185053    .0535232
             |
       _cons |  -3.368657   .0798065   -42.21   0.000    -3.525075    -3.21224
-------------+----------------------------------------------------------------
2            |
  female_lag |
          0  |  -.2985831    .325181    -0.92   0.359    -.9359261    .3387599
          1  |   1.724749   .5258295     3.28   0.001     .6941418    2.755356
             |
title_female |
          0  |  -.1533869   .0931808    -1.65   0.100     -.336018    .0292442
          1  |  -.2072921   .1685278    -1.23   0.219    -.5376005    .1230162
             |
  female_lag#|
title_female |
        0 0  |   .0524394   .1654172     0.32   0.751    -.2717723    .3766511
        0 1  |  -.1098583   .3306066    -0.33   0.740    -.7578353    .5381187
        1 0  |    .627917   .3511854     1.79   0.074    -.0603938    1.316228
        1 1  |   .0668895   .2770133     0.24   0.809    -.4760466    .6098257
             |
0.title_p_~y |   -.065459   .0643914    -1.02   0.309    -.1916637    .0607458
             |
  female_lag#|
title_p_ac~y |
        0 0  |   .1505761   .1419432     1.06   0.289    -.1276275    .4287797
        1 0  |  -.3832722   .2364115    -1.62   0.105    -.8466303    .0800859
             |
1.title_pe~y |  -.2161698   .1144424    -1.89   0.059    -.4404729    .0081332
             |
  female_lag#|
title_pers~y |
        0 1  |  -.2971434   .2559942    -1.16   0.246    -.7988828    .2045959
        1 1  |    .347079   .3166171     1.10   0.273    -.2734791    .9676372
             |
first_female |
          0  |   -.089773   .0617938    -1.45   0.146    -.2108866    .0313405
          1  |  -.2043526   .1188128    -1.72   0.085    -.4372214    .0285163
             |
  female_lag#|
first_female |
        0 0  |   .2292621   .1370659     1.67   0.094    -.0393822    .4979064
        0 1  |   .5174785   .2722322     1.90   0.057    -.0160869    1.051044
        1 0  |   .0925919   .2808446     0.33   0.742    -.4578534    .6430373
        1 1  |    .072579   .2823717     0.26   0.797    -.4808593    .6260173
             |
0.first_p_~y |   .1379074   .0689253     2.00   0.045     .0028162    .2729986
             |
  female_lag#|
first_p_ac~y |
        0 0  |   .2290905   .1612545     1.42   0.155    -.0869624    .5451434
        1 0  |  -.1055518   .2933763    -0.36   0.719    -.6805588    .4694553
             |
1.first_pe~y |  -.6406485   .0886977    -7.22   0.000    -.8144928   -.4668042
             |
  female_lag#|
first_pers~y |
        0 1  |  -.0216776   .1975278    -0.11   0.913    -.4088249    .3654697
        1 1  |   .5360983   .3282536     1.63   0.102     -.107267    1.179464
             |
group_p_ac~y |   3.278738   .1791371    18.30   0.000     2.927635     3.62984
             |
  female_lag#|
          c. |
group_p_ac~y |
          0  |   .1591211   .4263879     0.37   0.709    -.6765839    .9948261
          1  |  -1.967367   .7315744    -2.69   0.007    -3.401226   -.5335075
             |
group_pers~y |   11.48383   .2893576    39.69   0.000      10.9167    12.05096
             |
  female_lag#|
          c. |
group_pers~y |
          0  |   .5286766   .6090701     0.87   0.385    -.6650789    1.722432
          1  |  -3.974574   .9172735    -4.33   0.000    -5.772397   -2.176751
             |
job_rank_lag |
          1  |   .0595148   .1111046     0.54   0.592    -.1582462    .2772758
          2  |   .3043933   .1346965     2.26   0.024     .0403929    .5683936
          3  |   .0392839   .1362681     0.29   0.773    -.2277966    .3063644
          4  |   .4307455   .2383026     1.81   0.071     -.036319    .8978101
             |
  female_lag#|
job_rank_lag |
        0 1  |   -.044999   .2447049    -0.18   0.854    -.5246118    .4346138
        0 2  |   .2315881   .2985788     0.78   0.438    -.3536155    .8167917
        0 3  |  -.0897395   .2714359    -0.33   0.741    -.6217442    .4422651
        0 4  |  -.4708981   .2978722    -1.58   0.114    -1.054717    .1129206
        1 1  |   -.349636   .4305527    -0.81   0.417    -1.193504    .4942318
        1 2  |   -.697931   .5992239    -1.16   0.244    -1.872388    .4765262
        1 3  |  -.5792049   .5042448    -1.15   0.251    -1.567507    .4090969
        1 4  |  -.1043548    .527166    -0.20   0.843    -1.137581    .9288716
             |
    ln_post2 |   .2942346   .0286804    10.26   0.000      .238022    .3504472
             |
  female_lag#|
  c.ln_post2 |
          0  |  -.0195565    .058104    -0.34   0.736    -.1334382    .0943252
          1  |  -.0276163   .0973744    -0.28   0.777    -.2184667    .1632341
             |
       _cons |  -4.813874   .1431473   -33.63   0.000    -5.094437    -4.53331
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------
average ME

Average marginal effects                        Number of obs     =     27,913
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==1), predict(pr outcome(1))
2._predict   : Pr(transition==2), predict(pr outcome(2))
3._predict   : Pr(transition==3), predict(pr outcome(3))

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |  -.0050947   .0072799    -0.70   0.484    -.0193631    .0091737
          2  |   .0025158   .0046976     0.54   0.592    -.0066913     .011723
          3  |   .0025789    .007009     0.37   0.713    -.0111586    .0163163
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |  -.0239945   .0186865    -1.28   0.199    -.0606193    .0126303
          2  |   .0164188   .0113501     1.45   0.148    -.0058269    .0386645
          3  |   .0075757   .0179601     0.42   0.673    -.0276255    .0427769
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

scalars:
                 r(r1) =  0
                 r(r2) =  1.098612308502197
                 r(r3) =  1.386294364929199
                 r(r4) =  1.791759490966797
                 r(r5) =  2.079441547393799
                 r(r6) =  2.302585124969482
                 r(r7) =  2.484906673431396
                 r(r8) =  2.772588729858398
                 r(r9) =  3.091042518615723

Average marginal effects                        Number of obs     =     27,913
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==1), predict(pr outcome(1))
2._predict   : Pr(transition==2), predict(pr outcome(2))
3._predict   : Pr(transition==3), predict(pr outcome(3))

1._at        : ln_post2        =           0

2._at        : ln_post2        =    1.098612

3._at        : ln_post2        =    1.386294

4._at        : ln_post2        =    1.791759

5._at        : ln_post2        =    2.079442

6._at        : ln_post2        =    2.302585

7._at        : ln_post2        =    2.484907

8._at        : ln_post2        =    2.772589

9._at        : ln_post2        =    3.091043

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
_predict#_at |
        1 1  |  -.0095318   .0154321    -0.62   0.537    -.0397781    .0207146
        1 2  |  -.0073898   .0093774    -0.79   0.431    -.0257692    .0109896
        1 3  |  -.0067525   .0082763    -0.82   0.415    -.0229738    .0094688
        1 4  |  -.0057964   .0073933    -0.78   0.433    -.0202871    .0086942
        1 5  |  -.0050755   .0073652    -0.69   0.491    -.0195111    .0093601
        1 6  |  -.0044911   .0076931    -0.58   0.559    -.0195692     .010587
        1 7  |  -.0039969   .0081592    -0.49   0.624    -.0199886    .0119949
        1 8  |  -.0031857   .0091805    -0.35   0.729    -.0211792    .0148077
        1 9  |  -.0022418    .010602    -0.21   0.833    -.0230214    .0185378
        2 1  |   .0057554   .0078811     0.73   0.465    -.0096913    .0212021
        2 2  |   .0042536   .0056198     0.76   0.449     -.006761    .0152682
        2 3  |   .0037619   .0051346     0.73   0.464    -.0063018    .0138256
        2 4  |   .0029947   .0047263     0.63   0.526    -.0062687    .0122581
        2 5  |   .0023962   .0047408     0.51   0.613    -.0068955    .0116879
        2 6  |   .0019001   .0049661     0.38   0.702    -.0078333    .0116335
        2 7  |   .0014737   .0052911     0.28   0.781    -.0088966    .0118441
        2 8  |   .0007618   .0060386     0.13   0.900    -.0110738    .0125973
        2 9  |  -.0000834   .0071474    -0.01   0.991     -.014092    .0139252
        3 1  |   .0037763   .0158509     0.24   0.812    -.0272909    .0348435
        3 2  |   .0031362   .0093034     0.34   0.736    -.0150981    .0213705
        3 3  |   .0029906   .0081161     0.37   0.713    -.0129166    .0188979
        3 4  |   .0028017    .007158     0.39   0.695    -.0112277    .0168311
        3 5  |   .0026793   .0071043     0.38   0.706    -.0112449    .0166034
        3 6  |    .002591   .0074138     0.35   0.727    -.0119399    .0171219
        3 7  |   .0025232   .0078562     0.32   0.748    -.0128746    .0179209
        3 8  |    .002424   .0088087     0.28   0.783    -.0148408    .0196887
        3 9  |   .0023252   .0100945     0.23   0.818    -.0174595      .02211
-------------+----------------------------------------------------------------
1.female_lag |
_predict#_at |
        1 1  |   .0080884   .0344463     0.23   0.814     -.059425    .0756019
        1 2  |  -.0102763   .0234811    -0.44   0.662    -.0562984    .0357459
        1 3  |  -.0150489   .0213261    -0.71   0.480    -.0568473    .0267495
        1 4  |  -.0217412   .0191302    -1.14   0.256    -.0592357    .0157532
        1 5  |  -.0264625   .0183255    -1.44   0.149    -.0623797    .0094547
        1 6  |   -.030108   .0181925    -1.65   0.098    -.0657646    .0055487
        1 7  |  -.0330752   .0184097    -1.80   0.072    -.0691575    .0030071
        1 8  |  -.0377357   .0193168    -1.95   0.051    -.0755959    .0001245
        1 9  |   -.042863   .0210224    -2.04   0.041    -.0840661   -.0016599
        2 1  |   .0073728   .0158848     0.46   0.643     -.023761    .0385065
        2 2  |   .0115993   .0126148     0.92   0.358    -.0131252    .0363239
        2 3  |     .01293   .0119312     1.08   0.278    -.0104548    .0363148
        2 4  |   .0149769   .0114105     1.31   0.189    -.0073873    .0373411
        2 5  |   .0165558    .011523     1.44   0.151    -.0060289    .0391404
        2 6  |   .0178553   .0119599     1.49   0.135    -.0055857    .0412963
        2 7  |   .0189668   .0125615     1.51   0.131    -.0056533    .0435869
        2 8  |   .0208136    .013955     1.49   0.136    -.0065378     .048165
        2 9  |   .0229935   .0160857     1.43   0.153    -.0085339    .0545209
        3 1  |  -.0154612   .0343886    -0.45   0.653    -.0828615    .0519392
        3 2  |  -.0013231   .0227409    -0.06   0.954    -.0458944    .0432482
        3 3  |   .0021189    .020522     0.10   0.918    -.0381036    .0423414
        3 4  |   .0067643   .0183819     0.37   0.713    -.0292634    .0427921
        3 5  |   .0099068   .0177262     0.56   0.576     -.024836    .0446496
        3 6  |   .0122527   .0177538     0.69   0.490    -.0225441    .0470494
        3 7  |   .0141084   .0181124     0.78   0.436    -.0213912     .049608
        3 8  |   .0169221   .0192219     0.88   0.379    -.0207521    .0545964
        3 9  |   .0198694   .0210687     0.94   0.346    -.0214244    .0611633
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
*** Margins along Job Ladder ***
Students

Average marginal effects                        Number of obs     =     27,913
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==1), predict(pr outcome(1))
2._predict   : Pr(transition==2), predict(pr outcome(2))
3._predict   : Pr(transition==3), predict(pr outcome(3))
at           : job_rank_lag    =           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |  -.0060518   .0257779    -0.23   0.814    -.0565756    .0444719
          2  |   .0002157   .0177415     0.01   0.990     -.034557    .0349884
          3  |   .0058362   .0258215     0.23   0.821    -.0447731    .0564454
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |  -.0149003   .0563264    -0.26   0.791     -.125298    .0954974
          2  |  -.0082177   .0319714    -0.26   0.797    -.0708805     .054445
          3  |    .023118   .0544718     0.42   0.671    -.0836448    .1298808
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
JMC/Postdoc

Average marginal effects                        Number of obs     =     27,913
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==1), predict(pr outcome(1))
2._predict   : Pr(transition==2), predict(pr outcome(2))
3._predict   : Pr(transition==3), predict(pr outcome(3))
at           : job_rank_lag    =           2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |  -.0246268   .0328578    -0.75   0.454    -.0890268    .0397733
          2  |   .0274406    .026585     1.03   0.302    -.0246651    .0795463
          3  |  -.0028138   .0327009    -0.09   0.931    -.0669064    .0612788
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |   .0587637   .0548501     1.07   0.284    -.0487405    .1662678
          2  |  -.0475271   .0363492    -1.31   0.191    -.1187701     .023716
          3  |  -.0112366   .0566513    -0.20   0.843     -.122271    .0997979
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
Junior Faculty

Average marginal effects                        Number of obs     =     27,913
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==1), predict(pr outcome(1))
2._predict   : Pr(transition==2), predict(pr outcome(2))
3._predict   : Pr(transition==3), predict(pr outcome(3))
at           : job_rank_lag    =           3

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |  -.0063505   .0318014    -0.20   0.842    -.0686802    .0559791
          2  |  -.0027503   .0193348    -0.14   0.887    -.0406459    .0351452
          3  |   .0091009   .0311941     0.29   0.770    -.0520384    .0702402
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |   .0339241    .055994     0.61   0.545    -.0758221    .1436702
          2  |  -.0327882   .0323679    -1.01   0.311    -.0962281    .0306518
          3  |  -.0011359   .0536078    -0.02   0.983    -.1062053    .1039335
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
Senior Faculty

Average marginal effects                        Number of obs     =     27,913
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==1), predict(pr outcome(1))
2._predict   : Pr(transition==2), predict(pr outcome(2))
3._predict   : Pr(transition==3), predict(pr outcome(3))
at           : job_rank_lag    =           4

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |    .017351   .0376374     0.46   0.645    -.0564169    .0911189
          2  |  -.0381474   .0252082    -1.51   0.130    -.0875546    .0112597
          3  |   .0207965   .0359188     0.58   0.563    -.0496031     .091196
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |   .0269474   .0665797     0.40   0.686    -.1035464    .1574412
          2  |   .0025251   .0533096     0.05   0.962    -.1019599    .1070101
          3  |  -.0294725   .0601343    -0.49   0.624    -.1473335    .0883885
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
*** Margins under Different Initial Conditions ***

Average marginal effects                        Number of obs     =     27,913
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==1), predict(pr outcome(1))
2._predict   : Pr(transition==2), predict(pr outcome(2))
3._predict   : Pr(transition==3), predict(pr outcome(3))
at           : title_female    =           2
               title_p_acad_dummy=           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |  -.0077001   .0089874    -0.86   0.392    -.0253151    .0099149
          2  |   -.000511   .0062539    -0.08   0.935    -.0127685    .0117464
          3  |   .0082111   .0088338     0.93   0.353    -.0091029    .0255251
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |  -.0447214   .0212086    -2.11   0.035    -.0862896   -.0031532
          2  |    .027367   .0147083     1.86   0.063    -.0014606    .0561947
          3  |   .0173544   .0219072     0.79   0.428     -.025583    .0602918
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

Average marginal effects                        Number of obs     =     27,913
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==1), predict(pr outcome(1))
2._predict   : Pr(transition==2), predict(pr outcome(2))
3._predict   : Pr(transition==3), predict(pr outcome(3))
at           : title_female    =           1
               title_p_acad_dummy=           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .0020806   .0389919     0.05   0.957    -.0743421    .0785034
          2  |  -.0091851   .0211261    -0.43   0.664    -.0505914    .0322212
          3  |   .0071045   .0387427     0.18   0.855    -.0688298    .0830388
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |   .0180458    .040773     0.44   0.658    -.0618678    .0979594
          2  |   .0148334   .0218894     0.68   0.498    -.0280691    .0577359
          3  |  -.0328792   .0385553    -0.85   0.394    -.1084463    .0426879
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

Average marginal effects                        Number of obs     =     27,913
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==1), predict(pr outcome(1))
2._predict   : Pr(transition==2), predict(pr outcome(2))
3._predict   : Pr(transition==3), predict(pr outcome(3))
at           : title_female    =           0
               title_p_acad_dummy=           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .0033941   .0189266     0.18   0.858    -.0337013    .0404895
          2  |   .0007194   .0119314     0.06   0.952    -.0226656    .0241045
          3  |  -.0041135   .0186914    -0.22   0.826     -.040748     .032521
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |   .0301111    .049482     0.61   0.543     -.066872    .1270941
          2  |   .0591956   .0328142     1.80   0.071    -.0051191    .1235102
          3  |  -.0893067     .04651    -1.92   0.055    -.1804646    .0018513
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

Average marginal effects                        Number of obs     =     27,913
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==1), predict(pr outcome(1))
2._predict   : Pr(transition==2), predict(pr outcome(2))
3._predict   : Pr(transition==3), predict(pr outcome(3))
at           : title_female    =           2
               title_person_dummy=           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |  -.0593902   .0343215    -1.73   0.084    -.1266592    .0078788
          2  |   -.003824   .0148339    -0.26   0.797    -.0328978    .0252498
          3  |   .0632142   .0342769     1.84   0.065    -.0039673    .1303958
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |    -.16184   .0590996    -2.74   0.006    -.2776731   -.0460068
          2  |   .0695358    .035501     1.96   0.050    -.0000449    .1391165
          3  |   .0923042   .0565062     1.63   0.102    -.0184459    .2030542
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

Average marginal effects                        Number of obs     =     27,913
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==1), predict(pr outcome(1))
2._predict   : Pr(transition==2), predict(pr outcome(2))
3._predict   : Pr(transition==3), predict(pr outcome(3))
at           : title_female    =           1
               title_person_dummy=           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |  -.0519473   .0511073    -1.02   0.309    -.1521159    .0482212
          2  |  -.0112292   .0206346    -0.54   0.586    -.0516723     .029214
          3  |   .0631765   .0511015     1.24   0.216    -.0369806    .1633336
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |  -.0981265   .0687064    -1.43   0.153    -.2327887    .0365356
          2  |   .0533016   .0373609     1.43   0.154    -.0199244    .1265276
          3  |   .0448249   .0652023     0.69   0.492    -.0829691     .172619
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

Average marginal effects                        Number of obs     =     27,913
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==1), predict(pr outcome(1))
2._predict   : Pr(transition==2), predict(pr outcome(2))
3._predict   : Pr(transition==3), predict(pr outcome(3))
at           : title_female    =           0
               title_person_dummy=           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |  -.0485716   .0391369    -1.24   0.215    -.1252785    .0281352
          2  |  -.0026011   .0168603    -0.15   0.877    -.0356466    .0304445
          3  |   .0511727   .0394115     1.30   0.194    -.0260725    .1284179
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |  -.0862477   .0710467    -1.21   0.225    -.2254967    .0530013
          2  |   .1101682   .0502047     2.19   0.028     .0117687    .2085676
          3  |  -.0239205   .0687015    -0.35   0.728    -.1585729     .110732
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
***************************************************************
From Personal

Iteration 0:   log pseudolikelihood = -9943.9805  
Iteration 1:   log pseudolikelihood = -8016.3239  
Iteration 2:   log pseudolikelihood = -7973.8535  
Iteration 3:   log pseudolikelihood =  -7973.482  
Iteration 4:   log pseudolikelihood = -7973.4819  

Multinomial logistic regression                 Number of obs     =      9,299
                                                Wald chi2(94)     =    4271.53
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -7973.4819               Pseudo R2         =     0.1982

                          (Std. Err. adjusted for 3,708 clusters in title_id1)
------------------------------------------------------------------------------
             |               Robust
  transition |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
4            |
  female_lag |
          0  |  -.6846723   .3458675    -1.98   0.048     -1.36256   -.0067845
          1  |  -1.000578   .4942153    -2.02   0.043    -1.969223   -.0319342
             |
title_female |
          0  |   .1070702   .1122697     0.95   0.340    -.1129744    .3271149
          1  |  -.2314295   .1899812    -1.22   0.223    -.6037859    .1409268
             |
  female_lag#|
title_female |
        0 0  |   .1749642   .1756486     1.00   0.319    -.1693008    .5192292
        0 1  |   .4781745   .2998325     1.59   0.111    -.1094864    1.065835
        1 0  |  -.1760084   .2902816    -0.61   0.544    -.7449498    .3929331
        1 1  |   .1559475   .2586215     0.60   0.547    -.3509412    .6628363
             |
0.title_p_~y |   .1048066   .0867452     1.21   0.227    -.0652109    .2748241
             |
  female_lag#|
title_p_ac~y |
        0 0  |   .0571013   .1510154     0.38   0.705    -.2388836    .3530861
        1 0  |   .4075859   .2055399     1.98   0.047      .004735    .8104368
             |
1.title_pe~y |  -.0387121   .1077276    -0.36   0.719    -.2498543    .1724301
             |
  female_lag#|
title_pers~y |
        0 1  |   .0415711   .2087114     0.20   0.842    -.3674957    .4506379
        1 1  |  -.2730186   .2248914    -1.21   0.225    -.7137977    .1677605
             |
first_female |
          0  |   .0340716   .0898034     0.38   0.704    -.1419399     .210083
          1  |    -.13485   .1344154    -1.00   0.316    -.3982993    .1285994
             |
  female_lag#|
first_female |
        0 0  |  -.1492944   .1611272    -0.93   0.354     -.465098    .1665091
        0 1  |  -.0760431   .2903066    -0.26   0.793    -.6450336    .4929474
        1 0  |  -.3615092   .2469887    -1.46   0.143    -.8455982    .1225798
        1 1  |   .1077106   .2639802     0.41   0.683     -.409681    .6251022
             |
0.first_p_~y |   .4314889   .1032742     4.18   0.000     .2290753    .6339025
             |
  female_lag#|
first_p_ac~y |
        0 0  |   .3754305   .1929766     1.95   0.052    -.0027967    .7536578
        1 0  |   .2236924   .2685486     0.83   0.405    -.3026531    .7500379
             |
1.first_pe~y |  -.1896064   .1026709    -1.85   0.065    -.3908378    .0116249
             |
  female_lag#|
first_pers~y |
        0 1  |  -.3282283   .1966583    -1.67   0.095    -.7136715    .0572148
        1 1  |  -.0075838   .2586511    -0.03   0.977    -.5145306     .499363
             |
group_p_ac~y |   6.948316   .2565022    27.09   0.000     6.445581    7.451051
             |
  female_lag#|
          c. |
group_p_ac~y |
          0  |   1.153014    .465829     2.48   0.013     .2400058    2.066022
          1  |   1.552955   .6816392     2.28   0.023     .2169665    2.888943
             |
group_pers~y |   2.448961   .3224453     7.59   0.000     1.816979    3.080942
             |
  female_lag#|
          c. |
group_pers~y |
          0  |   .6661068   .5327751     1.25   0.211    -.3781133    1.710327
          1  |   .6305961   .6731188     0.94   0.349    -.6886924    1.949885
             |
job_rank_lag |
          1  |   .2221123   .1819055     1.22   0.222    -.1344159    .5786406
          2  |  -.0949061   .2235133    -0.42   0.671     -.532984    .3431718
          3  |    .265403   .2062389     1.29   0.198    -.1388177    .6696238
          4  |  -.2113287   .4734721    -0.45   0.655    -1.139317    .7166595
             |
  female_lag#|
job_rank_lag |
        0 1  |  -.2668071   .2904551    -0.92   0.358    -.8360887    .3024745
        0 2  |   .4983273   .3706434     1.34   0.179    -.2281204    1.224775
        0 3  |  -.2197951   .3188106    -0.69   0.491    -.8446524    .4050622
        0 4  |   .3390388   .5147029     0.66   0.510    -.6697604    1.347838
        1 1  |  -.7131035   .4128155    -1.73   0.084    -1.522207    .0960001
        1 2  |   .7604332   .4529006     1.68   0.093    -.1272357    1.648102
        1 3  |   .0680426   .4287329     0.16   0.874    -.7722585    .9083438
        1 4  |    1.40558   .9184364     1.53   0.126    -.3945226    3.205682
             |
    ln_post2 |   .1309167   .0387256     3.38   0.001     .0550159    .2068175
             |
  female_lag#|
  c.ln_post2 |
          0  |  -.0026075   .0668706    -0.04   0.969    -.1336715    .1284566
          1  |   .0668983   .0785129     0.85   0.394    -.0869841    .2207807
             |
       _cons |  -3.638018   .1888647   -19.26   0.000    -4.008186    -3.26785
-------------+----------------------------------------------------------------
5            |
  female_lag |
          0  |  -.8544693    .382088    -2.24   0.025    -1.603348   -.1055907
          1  |  -.5858414   .4620831    -1.27   0.205    -1.491508    .3198248
             |
title_female |
          0  |  -.1054558   .1217073    -0.87   0.386    -.3439977    .1330861
          1  |  -.1950879   .1622996    -1.20   0.229    -.5131892    .1230134
             |
  female_lag#|
title_female |
        0 0  |    .116406   .1972234     0.59   0.555    -.2701448    .5029569
        0 1  |   .0904885   .3203249     0.28   0.778    -.5373367    .7183138
        1 0  |  -.1635065   .2408114    -0.68   0.497    -.6354882    .3084752
        1 1  |   .0024862   .2414298     0.01   0.992    -.4707075      .47568
             |
0.title_p_~y |   .0768974   .0911283     0.84   0.399    -.1017108    .2555056
             |
  female_lag#|
title_p_ac~y |
        0 0  |   .0038569    .178733     0.02   0.983    -.3464533    .3541671
        1 0  |   .2574218    .199134     1.29   0.196    -.1328736    .6477173
             |
1.title_pe~y |   .0305993   .0990761     0.31   0.757    -.1635863    .2247848
             |
  female_lag#|
title_pers~y |
        0 1  |   .3355598   .2042985     1.64   0.100    -.0648578    .7359775
        1 1  |  -.3290221   .2088411    -1.58   0.115    -.7383433     .080299
             |
first_female |
          0  |  -.2155415   .1050144    -2.05   0.040    -.4213659   -.0097172
          1  |  -.0131652   .1265452    -0.10   0.917    -.2611892    .2348588
             |
  female_lag#|
first_female |
        0 0  |   .4984801   .1808935     2.76   0.006     .1439353    .8530249
        0 1  |     -.0053   .2917027    -0.02   0.986    -.5770269    .5664269
        1 0  |   .2299595   .2206922     1.04   0.297    -.2025892    .6625083
        1 1  |   .1476792   .2339089     0.63   0.528    -.3107738    .6061323
             |
0.first_p_~y |   .2202975   .1113528     1.98   0.048     .0020501    .4385449
             |
  female_lag#|
first_p_ac~y |
        0 0  |   .3794658   .2247631     1.69   0.091    -.0610617    .8199933
        1 0  |   .1168515    .265817     0.44   0.660    -.4041402    .6378433
             |
1.first_pe~y |  -.5229972   .1074482    -4.87   0.000    -.7335918   -.3124026
             |
  female_lag#|
first_pers~y |
        0 1  |  -.2251422   .2144846    -1.05   0.294    -.6455242    .1952398
        1 1  |  -.1236774   .2384704    -0.52   0.604    -.5910709    .3437161
             |
group_p_ac~y |   2.953882   .3019953     9.78   0.000     2.361983    3.545782
             |
  female_lag#|
          c. |
group_p_ac~y |
          0  |   .2496851   .5584401     0.45   0.655    -.8448375    1.344208
          1  |   1.045723   .6824124     1.53   0.125    -.2917804    2.383227
             |
group_pers~y |   6.874789   .3111441    22.10   0.000     6.264958    7.484621
             |
  female_lag#|
          c. |
group_pers~y |
          0  |  -.1846639   .5338598    -0.35   0.729     -1.23101     .861682
          1  |   .5841857   .6112642     0.96   0.339    -.6138701    1.782241
             |
job_rank_lag |
          1  |    .288737   .2173802     1.33   0.184    -.1373204    .7147944
          2  |   .0819051   .2552872     0.32   0.748    -.4184486    .5822587
          3  |  -.0440385   .2750722    -0.16   0.873      -.58317    .4950931
          4  |  -.0232998   .5047444    -0.05   0.963    -1.012581    .9659809
             |
  female_lag#|
job_rank_lag |
        0 1  |  -.2044244   .3542873    -0.58   0.564    -.8988147    .4899658
        0 2  |  -.6224146   .4743397    -1.31   0.189    -1.552103    .3072741
        0 3  |    .399519   .4236437     0.94   0.346    -.4308074    1.229845
        0 4  |  -.3695081   .5945202    -0.62   0.534    -1.534746      .79573
        1 1  |  -.3587785   .3714952    -0.97   0.334    -1.086896    .3693387
        1 2  |    .073539    .552792     0.13   0.894    -1.009913    1.156991
        1 3  |  -.2236689   .5360353    -0.42   0.676    -1.274279    .8269411
        1 4  |   1.112682   .9959225     1.12   0.264      -.83929    3.064654
             |
    ln_post2 |    .246419   .0427222     5.77   0.000     .1626851    .3301528
             |
  female_lag#|
  c.ln_post2 |
          0  |   .1240442   .0763477     1.62   0.104    -.0255945    .2736829
          1  |   .0521174   .0786928     0.66   0.508    -.1021178    .2063525
             |
       _cons |  -3.895336   .2097513   -18.57   0.000    -4.306441   -3.484231
-------------+----------------------------------------------------------------
6            |  (base outcome)
------------------------------------------------------------------------------
average ME

Average marginal effects                        Number of obs     =      9,299
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==4), predict(pr outcome(4))
2._predict   : Pr(transition==5), predict(pr outcome(5))
3._predict   : Pr(transition==6), predict(pr outcome(6))

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .0176449   .0110851     1.59   0.111    -.0040815    .0393714
          2  |  -.0336329   .0102725    -3.27   0.001    -.0537666   -.0134991
          3  |   .0159879   .0124546     1.28   0.199    -.0084227    .0403986
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |  -.0025734   .0150549    -0.17   0.864    -.0320805    .0269338
          2  |   .0143566   .0126198     1.14   0.255    -.0103778     .039091
          3  |  -.0117832    .015175    -0.78   0.437    -.0415257    .0179593
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
*** Margins along Job Ladder ***
Students

Average marginal effects                        Number of obs     =      9,299
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==4), predict(pr outcome(4))
2._predict   : Pr(transition==5), predict(pr outcome(5))
3._predict   : Pr(transition==6), predict(pr outcome(6))
at           : job_rank_lag    =           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |  -.0153387   .0430512    -0.36   0.722    -.0997176    .0690402
          2  |  -.0473012   .0451644    -1.05   0.295    -.1358217    .0412194
          3  |   .0626399    .050958     1.23   0.219    -.0372359    .1625157
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |   -.097379   .0557497    -1.75   0.081    -.2066465    .0118885
          2  |     .00562    .052053     0.11   0.914    -.0964021     .107642
          3  |    .091759   .0618675     1.48   0.138     -.029499    .2130171
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
JMC/Postdoc

Average marginal effects                        Number of obs     =      9,299
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==4), predict(pr outcome(4))
2._predict   : Pr(transition==5), predict(pr outcome(5))
3._predict   : Pr(transition==6), predict(pr outcome(6))
at           : job_rank_lag    =           2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .1323391   .0585696     2.26   0.024     .0175448    .2471334
          2  |  -.1271254   .0470054    -2.70   0.007    -.2192542   -.0349966
          3  |  -.0052137    .063278    -0.08   0.934    -.1292364    .1188089
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |   .1188288    .070683     1.68   0.093    -.0197074     .257365
          2  |  -.0249634    .070992    -0.35   0.725    -.1641051    .1141784
          3  |  -.0938655    .073884    -1.27   0.204    -.2386754    .0509445
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
Junior Faculty

Average marginal effects                        Number of obs     =      9,299
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==4), predict(pr outcome(4))
2._predict   : Pr(transition==5), predict(pr outcome(5))
3._predict   : Pr(transition==6), predict(pr outcome(6))
at           : job_rank_lag    =           3

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |  -.0454826   .0475457    -0.96   0.339    -.1386705    .0477053
          2  |   .0372061   .0541395     0.69   0.492    -.0689054    .1433176
          3  |   .0082765   .0577504     0.14   0.886    -.1049123    .1214653
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |   .0183124   .0649615     0.28   0.778    -.1090098    .1456345
          2  |  -.0195941    .059722    -0.33   0.743    -.1366471    .0974589
          3  |   .0012817   .0755203     0.02   0.986    -.1467354    .1492988
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
Senior Faculty

Average marginal effects                        Number of obs     =      9,299
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==4), predict(pr outcome(4))
2._predict   : Pr(transition==5), predict(pr outcome(5))
3._predict   : Pr(transition==6), predict(pr outcome(6))
at           : job_rank_lag    =           4

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .0908604   .0779752     1.17   0.244    -.0619682     .243689
          2  |  -.0921573   .0751729    -1.23   0.220    -.2394936     .055179
          3  |   .0012969   .0901258     0.01   0.989    -.1753465    .1779403
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |   .1422115   .1325291     1.07   0.283    -.1175408    .4019638
          2  |   .0832673   .1358216     0.61   0.540    -.1829381    .3494727
          3  |  -.2254788   .1302034    -1.73   0.083    -.4806728    .0297153
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
*** Margins under Different Initial Conditions ***

Average marginal effects                        Number of obs     =      9,299
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==4), predict(pr outcome(4))
2._predict   : Pr(transition==5), predict(pr outcome(5))
3._predict   : Pr(transition==6), predict(pr outcome(6))
at           : title_female    =           2
               title_p_acad_dummy=           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .0052794   .0162812     0.32   0.746    -.0266311    .0371899
          2  |  -.0326883   .0185795    -1.76   0.079    -.0691034    .0037269
          3  |   .0274089   .0201096     1.36   0.173    -.0120053     .066823
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |  -.0247694   .0211508    -1.17   0.242    -.0662243    .0166855
          2  |   .0056642    .022162     0.26   0.798    -.0377725    .0491009
          3  |   .0191052   .0243874     0.78   0.433    -.0286933    .0669037
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

Average marginal effects                        Number of obs     =      9,299
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==4), predict(pr outcome(4))
2._predict   : Pr(transition==5), predict(pr outcome(5))
3._predict   : Pr(transition==6), predict(pr outcome(6))
at           : title_female    =           1
               title_p_acad_dummy=           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |    .078336     .04648     1.69   0.092     -.012763    .1694351
          2  |  -.0422208    .037667    -1.12   0.262    -.1160468    .0316052
          3  |  -.0361152   .0496782    -0.73   0.467    -.1334827    .0612523
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |    .001935   .0380648     0.05   0.959    -.0726706    .0765405
          2  |  -.0026746   .0321624    -0.08   0.934    -.0657117    .0603626
          3  |   .0007396   .0440909     0.02   0.987     -.085677    .0871562
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

Average marginal effects                        Number of obs     =      9,299
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==4), predict(pr outcome(4))
2._predict   : Pr(transition==5), predict(pr outcome(5))
3._predict   : Pr(transition==6), predict(pr outcome(6))
at           : title_female    =           0
               title_p_acad_dummy=           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .0264834   .0297313     0.89   0.373    -.0317888    .0847556
          2  |  -.0243962   .0260207    -0.94   0.348    -.0753958    .0266035
          3  |  -.0020872   .0321333    -0.06   0.948    -.0650673    .0608928
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |  -.0452386   .0434126    -1.04   0.297    -.1303257    .0398485
          2  |  -.0077644   .0324644    -0.24   0.811    -.0713934    .0558645
          3  |   .0530031   .0428885     1.24   0.217    -.0310568    .1370629
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

Average marginal effects                        Number of obs     =      9,299
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==4), predict(pr outcome(4))
2._predict   : Pr(transition==5), predict(pr outcome(5))
3._predict   : Pr(transition==6), predict(pr outcome(6))
at           : title_female    =           2
               title_person_dummy=           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .0013996   .0288367     0.05   0.961    -.0551192    .0579184
          2  |  -.0027445   .0242312    -0.11   0.910    -.0502369    .0447479
          3  |   .0013449   .0302372     0.04   0.965    -.0579188    .0606086
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |  -.0221207   .0307357    -0.72   0.472    -.0823616    .0381202
          2  |  -.0061321    .024144    -0.25   0.800    -.0534535    .0411893
          3  |   .0282528   .0313245     0.90   0.367    -.0331421    .0896477
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

Average marginal effects                        Number of obs     =      9,299
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==4), predict(pr outcome(4))
2._predict   : Pr(transition==5), predict(pr outcome(5))
3._predict   : Pr(transition==6), predict(pr outcome(6))
at           : title_female    =           1
               title_person_dummy=           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .0762603   .0527513     1.45   0.148    -.0271303    .1796508
          2  |  -.0168675    .042611    -0.40   0.692    -.1003834    .0666484
          3  |  -.0593928   .0530324    -1.12   0.263    -.1633344    .0445489
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |   .0039569    .046037     0.09   0.932     -.086274    .0941878
          2  |  -.0136929   .0340574    -0.40   0.688    -.0804443    .0530585
          3  |    .009736   .0510339     0.19   0.849    -.0902887    .1097607
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

Average marginal effects                        Number of obs     =      9,299
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==4), predict(pr outcome(4))
2._predict   : Pr(transition==5), predict(pr outcome(5))
3._predict   : Pr(transition==6), predict(pr outcome(6))
at           : title_female    =           0
               title_person_dummy=           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .0228703   .0391541     0.58   0.559    -.0538703    .0996108
          2  |   .0032291   .0310559     0.10   0.917    -.0576394    .0640977
          3  |  -.0260994   .0392024    -0.67   0.506    -.1029346    .0507358
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |   -.043498   .0537621    -0.81   0.418    -.1488699    .0618738
          2  |  -.0180614   .0358944    -0.50   0.615     -.088413    .0522902
          3  |   .0615594   .0513505     1.20   0.231    -.0390857    .1622045
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
***************************************************************
From Others

note: 1.female_lag#1.job_rank_lag identifies no observations in the sample
Iteration 0:   log pseudolikelihood = -25801.166  
Iteration 1:   log pseudolikelihood = -20876.666  
Iteration 2:   log pseudolikelihood = -20499.378  
Iteration 3:   log pseudolikelihood = -20492.293  
Iteration 4:   log pseudolikelihood = -20492.031  
Iteration 5:   log pseudolikelihood = -20491.966  
Iteration 6:   log pseudolikelihood = -20491.953  
Iteration 7:   log pseudolikelihood =  -20491.95  
Iteration 8:   log pseudolikelihood =  -20491.95  
Iteration 9:   log pseudolikelihood =  -20491.95  
Iteration 10:  log pseudolikelihood =  -20491.95  
Iteration 11:  log pseudolikelihood =  -20491.95  

Multinomial logistic regression                 Number of obs     =     26,564
                                                Wald chi2(91)     =          .
                                                Prob > chi2       =          .
Log pseudolikelihood =  -20491.95               Pseudo R2         =     0.2058

                          (Std. Err. adjusted for 5,549 clusters in title_id1)
------------------------------------------------------------------------------
             |               Robust
  transition |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
7            |
  female_lag |
          0  |  -.5110809   .1970128    -2.59   0.009    -.8972188   -.1249429
          1  |  -.2089591   .3950818    -0.53   0.597    -.9833051    .5653869
             |
title_female |
          0  |  -.0162654   .0466136    -0.35   0.727    -.1076264    .0750957
          1  |   .0419504   .0806907     0.52   0.603    -.1162005    .2001013
             |
  female_lag#|
title_female |
        0 0  |   .0037151   .1184587     0.03   0.975    -.2284598    .2358899
        0 1  |   .0487723   .2290417     0.21   0.831    -.4001413    .4976858
        1 0  |   .2775168   .3126592     0.89   0.375     -.335284    .8903177
        1 1  |   .3026341   .2307214     1.31   0.190    -.1495716    .7548398
             |
0.title_p_~y |  -.0107233     .03367    -0.32   0.750    -.0767153    .0552686
             |
  female_lag#|
title_p_ac~y |
        0 0  |    -.02804   .0957004    -0.29   0.770    -.2156092    .1595292
        1 0  |   .3890702   .2033094     1.91   0.056    -.0094089    .7875493
             |
1.title_pe~y |  -.0720727   .0608518    -1.18   0.236    -.1913401    .0471947
             |
  female_lag#|
title_pers~y |
        0 1  |   .2953622   .1753981     1.68   0.092    -.0484119    .6391362
        1 1  |  -.0368318   .2617826    -0.14   0.888    -.5499164    .4762527
             |
first_female |
          0  |  -.0234695   .0386353    -0.61   0.544    -.0991934    .0522543
          1  |   .1439921   .0571854     2.52   0.012     .0319109    .2560734
             |
  female_lag#|
first_female |
        0 0  |   .0829216   .0986483     0.84   0.401    -.1104254    .2762686
        0 1  |   .0159166   .1874695     0.08   0.932    -.3515168      .38335
        1 0  |   .0266651   .2800544     0.10   0.924    -.5222315    .5755617
        1 1  |  -.6440099   .2271434    -2.84   0.005    -1.089203   -.1988171
             |
0.first_p_~y |   .4134093   .0376456    10.98   0.000     .3396253    .4871933
             |
  female_lag#|
first_p_ac~y |
        0 0  |   .2266681   .1084523     2.09   0.037     .0141055    .4392308
        1 0  |   -.270923   .2284046    -1.19   0.236    -.7185878    .1767417
             |
1.first_pe~y |   -.062389   .0478649    -1.30   0.192    -.1562025    .0314244
             |
  female_lag#|
first_pers~y |
        0 1  |  -.2052341    .135706    -1.51   0.130    -.4712129    .0607447
        1 1  |   .2992003   .2575612     1.16   0.245    -.2056104    .8040111
             |
group_p_ac~y |   6.548036   .1051741    62.26   0.000     6.341899    6.754174
             |
  female_lag#|
          c. |
group_p_ac~y |
          0  |   .7324267   .2855091     2.57   0.010     .1728392    1.292014
          1  |   1.184924   .5859484     2.02   0.043     .0364857    2.333361
             |
group_pers~y |   2.287965   .1764191    12.97   0.000      1.94219     2.63374
             |
  female_lag#|
          c. |
group_pers~y |
          0  |   .3771022    .503143     0.75   0.454    -.6090399    1.363244
          1  |  -.0290809   .8203029    -0.04   0.972    -1.636845    1.578683
             |
job_rank_lag |
          1  |   .1397025   .6543145     0.21   0.831     -1.14273    1.422135
          2  |  -.5880465   .4657937    -1.26   0.207    -1.500985    .3248923
          3  |  -1.785789   1.352352    -1.32   0.187    -4.436349    .8647716
          4  |  -.5635306   .6448109    -0.87   0.382    -1.827337    .7002755
             |
  female_lag#|
job_rank_lag |
        0 1  |  -.5736821   .6811382    -0.84   0.400    -1.908688    .7613241
        0 2  |   .4350765   .6067372     0.72   0.473    -.7541067     1.62426
        0 3  |   1.452687   1.368799     1.06   0.289     -1.23011    4.135484
        0 4  |   .8391418   .6646265     1.26   0.207    -.4635022    2.141786
        1 1  |          0  (empty)
        1 2  |   .8570056   .9949956     0.86   0.389     -1.09315    2.807161
        1 3  |   .7325716   1.505392     0.49   0.627    -2.217943    3.683086
        1 4  |   1.212333   .7790986     1.56   0.120    -.3146724    2.739338
             |
    ln_post2 |    .064363   .0189055     3.40   0.001     .0273088    .1014172
             |
  female_lag#|
  c.ln_post2 |
          0  |   .0653504   .0503736     1.30   0.195    -.0333801    .1640808
          1  |  -.1111822   .0930064    -1.20   0.232    -.2934714    .0711069
             |
       _cons |  -3.275829   .0705823   -46.41   0.000    -3.414168    -3.13749
-------------+----------------------------------------------------------------
8            |
  female_lag |
          0  |  -.1086642   .2643449    -0.41   0.681    -.6267706    .4094422
          1  |   .6145996   .4237467     1.45   0.147    -.2159286    1.445128
             |
title_female |
          0  |   .0282192   .0588321     0.48   0.631    -.0870896     .143528
          1  |   .0165146   .0970187     0.17   0.865    -.1736386    .2066678
             |
  female_lag#|
title_female |
        0 0  |   .0194585     .15295     0.13   0.899    -.2803179    .3192349
        0 1  |  -.1584265   .2778433    -0.57   0.569    -.7029893    .3861363
        1 0  |   .3661433   .3068863     1.19   0.233    -.2353428    .9676294
        1 1  |   .0575412    .240567     0.24   0.811    -.4139615    .5290439
             |
0.title_p_~y |  -.0000485   .0463498    -0.00   0.999    -.0908923    .0907954
             |
  female_lag#|
title_p_ac~y |
        0 0  |   .1843358   .1314851     1.40   0.161    -.0733701    .4420418
        1 0  |   .2494643   .2065066     1.21   0.227    -.1552812    .6542099
             |
1.title_pe~y |  -.1501084   .0711396    -2.11   0.035    -.2895395   -.0106774
             |
  female_lag#|
title_pers~y |
        0 1  |   .1569568   .2001542     0.78   0.433    -.2353382    .5492518
        1 1  |  -.0682165   .2564403    -0.27   0.790    -.5708303    .4343972
             |
first_female |
          0  |   -.023963   .0552858    -0.43   0.665    -.1323213    .0843953
          1  |    -.11615   .0841638    -1.38   0.168    -.2811081    .0488081
             |
  female_lag#|
first_female |
        0 0  |  -.0070383    .142334    -0.05   0.961    -.2860079    .2719312
        0 1  |   -.163419   .2229928    -0.73   0.464    -.6004769    .2736389
        1 0  |   .0025989    .283013     0.01   0.993    -.5520963    .5572941
        1 1  |   .1117225   .2233379     0.50   0.617    -.3260118    .5494568
             |
0.first_p_~y |   .2319352   .0533173     4.35   0.000     .1274353    .3364352
             |
  female_lag#|
first_p_ac~y |
        0 0  |  -.1287234   .1554546    -0.83   0.408    -.4334088    .1759619
        1 0  |  -.1662318   .2413281    -0.69   0.491    -.6392261    .3067625
             |
1.first_pe~y |  -.6772957   .0648529   -10.44   0.000     -.804405   -.5501864
             |
  female_lag#|
first_pers~y |
        0 1  |  -.0936288   .1736524    -0.54   0.590    -.4339813    .2467237
        1 1  |   .0191371   .2460614     0.08   0.938    -.4631343    .5014085
             |
group_p_ac~y |   2.689155   .1514427    17.76   0.000     2.392333    2.985977
             |
  female_lag#|
          c. |
group_p_ac~y |
          0  |   .5372568   .4198706     1.28   0.201    -.2856744    1.360188
          1  |    .467813   .7180719     0.65   0.515    -.9395821    1.875208
             |
group_pers~y |   9.863174   .2022723    48.76   0.000     9.466728    10.25962
             |
  female_lag#|
          c. |
group_pers~y |
          0  |   .7685478   .5326209     1.44   0.149      -.27537    1.812466
          1  |  -1.444311   .7483176    -1.93   0.054    -2.910987    .0223646
             |
job_rank_lag |
          1  |  -.1202537   1.034062    -0.12   0.907    -2.146978    1.906471
          2  |  -15.61061   .3417551   -45.68   0.000    -16.28044   -14.94079
          3  |  -16.93077   .3695334   -45.82   0.000    -17.65505    -16.2065
          4  |   .9864476   1.214972     0.81   0.417    -1.394854    3.367749
             |
  female_lag#|
job_rank_lag |
        0 1  |   22.17765   1.416819    15.65   0.000     19.40074    24.95456
        0 2  |  -.0956565   .5874879    -0.16   0.871    -1.247112    1.055799
        0 3  |   16.73924   .5173115    32.36   0.000     15.72533    17.75315
        0 4  |  -.8475907   1.241649    -0.68   0.495    -3.281178    1.585996
        1 1  |          0  (empty)
        1 2  |   .1494024   1.319957     0.11   0.910    -2.437665     2.73647
        1 3  |   .5844016   .6587253     0.89   0.375    -.7066763    1.875479
        1 4  |  -.8858926   1.318018    -0.67   0.501    -3.469161    1.697376
             |
    ln_post2 |   .2211398   .0262218     8.43   0.000     .1697461    .2725336
             |
  female_lag#|
  c.ln_post2 |
          0  |   -.086108   .0664028    -1.30   0.195    -.2162552    .0440392
          1  |  -.2119893   .0973358    -2.18   0.029    -.4027639   -.0212147
             |
       _cons |  -4.134198   .0998433   -41.41   0.000    -4.329888   -3.938509
-------------+----------------------------------------------------------------
9            |  (base outcome)
------------------------------------------------------------------------------
Note: 1 observation completely determined.  Standard errors questionable.
average ME

Average marginal effects                        Number of obs     =     26,564
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==7), predict(pr outcome(7))
2._predict   : Pr(transition==8), predict(pr outcome(8))
3._predict   : Pr(transition==9), predict(pr outcome(9))

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .0085009   .0075649     1.12   0.261    -.0063261    .0233279
          2  |  -.0011008   .0058291    -0.19   0.850    -.0125257     .010324
          3  |  -.0074001    .008053    -0.92   0.358    -.0231836    .0083835
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |          .  (not estimable)
          2  |          .  (not estimable)
          3  |          .  (not estimable)
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
*** Margins along Job Ladder ***
Students

Average marginal effects                        Number of obs     =     26,564
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==7), predict(pr outcome(7))
2._predict   : Pr(transition==8), predict(pr outcome(8))
3._predict   : Pr(transition==9), predict(pr outcome(9))
at           : job_rank_lag    =           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |  -.3695485   .1051277    -3.52   0.000     -.575595   -.1635019
          2  |   .8836531   .0781189    11.31   0.000     .7305429    1.036763
          3  |  -.5141047    .126096    -4.08   0.000    -.7612482   -.2669611
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |          .  (not estimable)
          2  |          .  (not estimable)
          3  |          .  (not estimable)
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
JMC/Postdoc

Average marginal effects                        Number of obs     =     26,564
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==7), predict(pr outcome(7))
2._predict   : Pr(transition==8), predict(pr outcome(8))
3._predict   : Pr(transition==9), predict(pr outcome(9))
at           : job_rank_lag    =           2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .0808319   .0985471     0.82   0.412    -.1123169    .2739807
          2  |  -5.59e-09   2.67e-08    -0.21   0.834    -5.79e-08    4.67e-08
          3  |  -.0808319   .0985471    -0.82   0.412    -.2739807     .112317
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |   .1615488   .1676468     0.96   0.335    -.1670328    .4901304
          2  |  -1.07e-08   4.68e-08    -0.23   0.819    -1.03e-07    8.11e-08
          3  |  -.1615488   .1676468    -0.96   0.335    -.4901305    .1670329
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
Junior Faculty

Average marginal effects                        Number of obs     =     26,564
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==7), predict(pr outcome(7))
2._predict   : Pr(transition==8), predict(pr outcome(8))
3._predict   : Pr(transition==9), predict(pr outcome(9))
at           : job_rank_lag    =           3

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .1651143   .1352921     1.22   0.222    -.1000533    .4302819
          2  |   .1240358   .0286671     4.33   0.000     .0678493    .1802222
          3  |  -.2891501   .1363155    -2.12   0.034    -.5563235   -.0219766
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |   .1074066    .158405     0.68   0.498    -.2030615    .4178747
          2  |   8.10e-09   1.37e-08     0.59   0.554    -1.87e-08    3.49e-08
          3  |  -.1074066    .158405    -0.68   0.498    -.4178747    .2030615
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
Senior Faculty

Average marginal effects                        Number of obs     =     26,564
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==7), predict(pr outcome(7))
2._predict   : Pr(transition==8), predict(pr outcome(8))
3._predict   : Pr(transition==9), predict(pr outcome(9))
at           : job_rank_lag    =           4

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .1756592   .0836686     2.10   0.036     .0116719    .3396466
          2  |  -.1452124   .1839542    -0.79   0.430     -.505756    .2153312
          3  |  -.0304468   .1719168    -0.18   0.859    -.3673975    .3065038
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |   .2457866   .1131195     2.17   0.030     .0240764    .4674967
          2  |  -.1521778   .1894985    -0.80   0.422    -.5235881    .2192325
          3  |  -.0936087   .1819093    -0.51   0.607    -.4501444     .262927
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
*** Margins under Different Initial Conditions ***

Average marginal effects                        Number of obs     =     26,564
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==7), predict(pr outcome(7))
2._predict   : Pr(transition==8), predict(pr outcome(8))
3._predict   : Pr(transition==9), predict(pr outcome(9))
at           : title_female    =           2
               title_p_acad_dummy=           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .0128624   .0109814     1.17   0.241    -.0086607    .0343856
          2  |  -.0099917   .0086658    -1.15   0.249    -.0269763    .0069928
          3  |  -.0028707   .0116712    -0.25   0.806    -.0257459    .0200044
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |          .  (not estimable)
          2  |          .  (not estimable)
          3  |          .  (not estimable)
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

Average marginal effects                        Number of obs     =     26,564
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==7), predict(pr outcome(7))
2._predict   : Pr(transition==8), predict(pr outcome(8))
3._predict   : Pr(transition==9), predict(pr outcome(9))
at           : title_female    =           1
               title_p_acad_dummy=           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .0260527   .0373045     0.70   0.485    -.0470628    .0991681
          2  |  -.0241219   .0201981    -1.19   0.232    -.0637095    .0154657
          3  |  -.0019308   .0381449    -0.05   0.960    -.0766934    .0728318
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |          .  (not estimable)
          2  |          .  (not estimable)
          3  |          .  (not estimable)
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

Average marginal effects                        Number of obs     =     26,564
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==7), predict(pr outcome(7))
2._predict   : Pr(transition==8), predict(pr outcome(8))
3._predict   : Pr(transition==9), predict(pr outcome(9))
at           : title_female    =           0
               title_p_acad_dummy=           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .0129243   .0202924     0.64   0.524     -.026848    .0526966
          2  |  -.0086946   .0132342    -0.66   0.511    -.0346331    .0172439
          3  |  -.0042298   .0207803    -0.20   0.839    -.0449584    .0364989
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |          .  (not estimable)
          2  |          .  (not estimable)
          3  |          .  (not estimable)
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

Average marginal effects                        Number of obs     =     26,564
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==7), predict(pr outcome(7))
2._predict   : Pr(transition==8), predict(pr outcome(8))
3._predict   : Pr(transition==9), predict(pr outcome(9))
at           : title_female    =           2
               title_person_dummy=           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .0491613   .0271297     1.81   0.070    -.0040118    .1023345
          2  |   .0028885   .0145037     0.20   0.842    -.0255383    .0313153
          3  |  -.0520499   .0279021    -1.87   0.062     -.106737    .0026372
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |          .  (not estimable)
          2  |          .  (not estimable)
          3  |          .  (not estimable)
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

Average marginal effects                        Number of obs     =     26,564
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==7), predict(pr outcome(7))
2._predict   : Pr(transition==8), predict(pr outcome(8))
3._predict   : Pr(transition==9), predict(pr outcome(9))
at           : title_female    =           1
               title_person_dummy=           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .0628895   .0417689     1.51   0.132     -.018976     .144755
          2  |  -.0116022   .0229955    -0.50   0.614    -.0566725    .0334681
          3  |  -.0512873   .0432503    -1.19   0.236    -.1360564    .0334818
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |          .  (not estimable)
          2  |          .  (not estimable)
          3  |          .  (not estimable)
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

Average marginal effects                        Number of obs     =     26,564
Model VCE    : Robust

dy/dx w.r.t. : 0.female_lag 1.female_lag
1._predict   : Pr(transition==7), predict(pr outcome(7))
2._predict   : Pr(transition==8), predict(pr outcome(8))
3._predict   : Pr(transition==9), predict(pr outcome(9))
at           : title_female    =           0
               title_person_dummy=           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.female_lag |
    _predict |
          1  |   .0489706   .0331102     1.48   0.139    -.0159242    .1138654
          2  |   .0044716    .018655     0.24   0.811    -.0320916    .0410348
          3  |  -.0534422   .0329817    -1.62   0.105    -.1180851    .0112008
-------------+----------------------------------------------------------------
1.female_lag |
    _predict |
          1  |          .  (not estimable)
          2  |          .  (not estimable)
          3  |          .  (not estimable)
-------------+----------------------------------------------------------------
2.female_lag |  (base outcome)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. log close
      name:  <unnamed>
       log:  /accounts/grad/haowen.wu/Documents/EJR-revise/dynamic/check_May201
> 9/mlogit-full-after-Aug2017.log
  log type:  text
 closed on:  25 May 2019, 21:04:46
-------------------------------------------------------------------------------
